Quality of life in patients with epilepsy caused by hippocampal sclerosis: a comparison of surgical and non-surgical approaches
Bibliographic record
Abstract
Background. Epilepsy associated with hippocampal sclerosis is a significant neurological issue that substantially impairs patients’ quality of life. Surgical treatment is considered an effective method for improving people’s state; however, its impact on quality of life remains underexplored. Objective: to assess the impact of surgical intervention on the quality of life of patients with epilepsy caused by hippocampal sclerosis by studying the factors that influence it and comparing outcomes between operated and non-operated patients. Materials and methods. The study involved 100 patients treated at the Regional Clinical Center of Neurosurgery and Neurology in Uzhhorod from 2014 to 2020. Quality of life was assessed using the QOLIE-31-P scale, cognitive and emotional functions were evaluated using the Montreal Cognitive Assessment Test, Beck Depression Inventory, and other methods. Statistical analysis was performed using the t-test, Pearson correlation coefficient, and chi-square test. Results. Patients who underwent surgical treatment for hippocampal sclerosis showed a higher level of overall quality of life: 66.0 ± 14.8 compared to 58.2 ± 13.3 in non-operated patients (p = 0.008). Disease duration before intervention and patient’s age at the time of surgery correlated with quality of life (r = –0.45, p < 0.01; r = –0.42, p < 0.01, respectively). The number of epileptiform discharges on preoperative EEG correlated with poorer quality of life (r = –0.36, p < 0.05). Polytherapy had mixed effects: negative one on cognitive functions but positive one on anxiety and depression levels. Conclusions. Surgery can improve the quality of life in patients with hippocampal sclerosis. Early surgical intervention may lead to better outcomes highlighting the importance of timely and individualized treatment approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".